[Dataset] Add Datasets BBBP, ToxCast, SIDER, ClinTox (#86)

* Fix

* Update

* Fix

* Add MUV

* Add HIV

* Add BACE

* Update

docs/source/api/data.rst

@@ -65,6 +65,34 @@ BACE
     :members: __getitem__, __len__, task_pos_weights
     :show-inheritance:
 
+BBBP
+````
+
+.. autoclass:: dgllife.data.BBBP
+    :members: __getitem__, __len__, task_pos_weights
+    :show-inheritance:
+
+ToxCast
+```````
+
+.. autoclass:: dgllife.data.ToxCast
+    :members: __getitem__, __len__, task_pos_weights
+    :show-inheritance:
+
+SIDER
+`````
+
+.. autoclass:: dgllife.data.SIDER
+    :members: __getitem__, __len__, task_pos_weights
+    :show-inheritance:
+
+ClinTox
+```````
+
+.. autoclass:: dgllife.data.ClinTox
+    :members: __getitem__, __len__, task_pos_weights
+    :show-inheritance:
+
 Experimental solubility determined at AstraZeneca, extracted from ChEMBL
 ````````````````````````````````````````````````````````````````````````
 

python/dgllife/data/__init__.py

@@ -17,4 +17,8 @@
 from .muv import *
 from .hiv import *
 from .bace import *
+from .bbbp import *
+from .toxcast import *
+from .sider import *
+from .clintox import *
 from .uspto import *

python/dgllife/data/bbbp.py

@@ -0,0 +1,155 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# BBBP from MoleculeNet for the prediction of permeability properties.
+
+import pandas as pd
+
+from dgl.data.utils import get_download_dir, download, _get_dgl_url, extract_archive
+
+from .csv_dataset import MoleculeCSVDataset
+from ..utils.mol_to_graph import smiles_to_bigraph
+
+__all__ = ['BBBP']
+
+class BBBP(MoleculeCSVDataset):
+    r"""BBBP from MoleculeNet for the prediction of permeability properties
+
+    The Blood-brain barrier penetration (BBBP) dataset comes from a study on
+    the modeling and prediction of the barrier permeability. As a membrane
+    separating circulating blood and brain extracellular fluid, the blood-brain
+    barrier blocks most drugs, hormones and neurotransmitters. Thus penetration of
+    the barrier forms a long-standing issue in development of drugs targeting
+    central nervous system. This dataset includes binary labels for over 2000
+    compounds on their permeability properties.
+
+    References:
+
+        * [1] MoleculeNet: A Benchmark for Molecular Machine Learning.
+        * [2] A Bayesian approach to in silico blood-brain barrier penetration modeling
+
+    Parameters
+    ----------
+    smiles_to_graph: callable, str -> DGLGraph
+        A function turning a SMILES string into a DGLGraph.
+        Default to :func:`dgllife.utils.smiles_to_bigraph`.
+    node_featurizer : callable, rdkit.Chem.rdchem.Mol -> dict
+        Featurization for nodes like atoms in a molecule, which can be used to update
+        ndata for a DGLGraph. Default to None.
+    edge_featurizer : callable, rdkit.Chem.rdchem.Mol -> dict
+        Featurization for edges like bonds in a molecule, which can be used to update
+        edata for a DGLGraph. Default to None.
+    load : bool
+        Whether to load the previously pre-processed dataset or pre-process from scratch.
+        ``load`` should be False when we want to try different graph construction and
+        featurization methods and need to preprocess from scratch. Default to False.
+    log_every : bool
+        Print a message every time ``log_every`` molecules are processed. Default to 1000.
+    cache_file_path : str
+        Path to the cached DGLGraphs, default to 'bbbp_dglgraph.bin'.
+    n_jobs : int
+        The maximum number of concurrently running jobs for graph construction and featurization,
+        using joblib backend. Default to 1.
+
+    Examples
+    --------
+
+    >>> import torch
+    >>> from dgllife.data import BBBP
+    >>> from dgllife.utils import smiles_to_bigraph, CanonicalAtomFeaturizer
+
+    >>> dataset = BBBP(smiles_to_bigraph, CanonicalAtomFeaturizer())
+    >>> # Get size of the dataset
+    >>> len(dataset)
+    2050
+    >>> # Get the 0th datapoint, consisting of SMILES, DGLGraph, labels, and masks
+    >>> dataset[0]
+    ('[Cl].CC(C)NCC(O)COc1cccc2ccccc12',
+     Graph(num_nodes=20, num_edges=40,
+           ndata_schemes={'h': Scheme(shape=(74,), dtype=torch.float32)}
+           edata_schemes={}),
+     tensor([1.]),
+     tensor([1.]))
+
+    The dataset instance also contains information about compound name.
+
+    >>> dataset.names[i]
+
+    We can also get the name along with SMILES, DGLGraph, labels, and masks at once.
+
+    >>> dataset.load_full = True
+    >>> dataset[0]
+    ('[Cl].CC(C)NCC(O)COc1cccc2ccccc12',
+     Graph(num_nodes=20, num_edges=40,
+           ndata_schemes={'h': Scheme(shape=(74,), dtype=torch.float32)}
+           edata_schemes={}),
+     tensor([1.]),
+     tensor([1.]),
+     'Propanolol')
+
+    To address the imbalance between positive and negative samples, we can re-weight
+    positive samples for each task based on the training datapoints.
+
+    >>> train_ids = torch.arange(500)
+    >>> dataset.task_pos_weights(train_ids)
+    tensor([0.7123])
+    """
+    def __init__(self,
+                 smiles_to_graph=smiles_to_bigraph,
+                 node_featurizer=None,
+                 edge_featurizer=None,
+                 load=False,
+                 log_every=1000,
+                 cache_file_path='./bbbp_dglgraph.bin',
+                 n_jobs=1):
+
+        self._url = 'dataset/bbbp.zip'
+        data_path = get_download_dir() + '/bbbp.zip'
+        dir_path = get_download_dir() + '/bbbp'
+        download(_get_dgl_url(self._url), path=data_path, overwrite=False)
+        extract_archive(data_path, dir_path)
+        df = pd.read_csv(dir_path + '/BBBP.csv')
+
+        self.names = df['name'].tolist()
+        self.load_full = False
+
+        super(BBBP, self).__init__(df=df,
+                                   smiles_to_graph=smiles_to_graph,
+                                   node_featurizer=node_featurizer,
+                                   edge_featurizer=edge_featurizer,
+                                   smiles_column='smiles',
+                                   cache_file_path=cache_file_path,
+                                   task_names=['p_np'],
+                                   load=load,
+                                   log_every=log_every,
+                                   init_mask=True,
+                                   n_jobs=n_jobs)
+
+    def __getitem__(self, item):
+        """Get datapoint with index
+
+        Parameters
+        ----------
+        item : int
+            Datapoint index
+
+        Returns
+        -------
+        str
+            SMILES for the ith datapoint
+        DGLGraph
+            DGLGraph for the ith datapoint
+        Tensor of dtype float32 and shape (T)
+            Labels of the ith datapoint for all tasks. T for the number of tasks.
+        Tensor of dtype float32 and shape (T)
+            Binary masks of the ith datapoint indicating the existence of labels for all tasks.
+        str, optional
+            Name for the ith compound, returned only when ``self.load_full`` is True.
+        """
+        if self.load_full:
+            return self.smiles[item], self.graphs[item], self.labels[item], \
+                   self.mask[item], self.names[item]
+        else:
+            return self.smiles[item], self.graphs[item], self.labels[item], self.mask[item]

python/dgllife/data/clintox.py

@@ -0,0 +1,130 @@
+# -*- coding: utf-8 -*-
+#
+# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# ClinTox from MoleculeNet for the prediction of clinical trial toxicity
+# (or absence of toxicity) and FDA approval status
+
+import pandas as pd
+
+from dgl.data.utils import get_download_dir, download, _get_dgl_url, extract_archive
+
+from .csv_dataset import MoleculeCSVDataset
+from ..utils.mol_to_graph import smiles_to_bigraph
+
+__all__ = ['ClinTox']
+
+class ClinTox(MoleculeCSVDataset):
+    r"""ClinTox from MoleculeNet for the prediction of clinical trial toxicity
+    (or absence of toxicity) and FDA approval status
+
+    The ClinTox dataset compares drugs approved by the FDA and drugs that have failed
+    clinical trials for toxicity reasons. The dataset includes two classification
+    tasks for 1491 drug compounds with known chemical structures: (1) clinical trial
+    toxicity (or absence of toxicity) and (2) FDA approval status. The MoleculeNet
+    benchmark compiles the list of FDA-approved drugs from the SWEETLEAD database and
+    the list of drugs that failed clinical trials for toxicity reasons from the
+    Aggregate Analysis of ClinicalTrials.gov (AACT) database.
+
+    References:
+
+        * [1] MoleculeNet: A Benchmark for Molecular Machine Learning.
+
+    Parameters
+    ----------
+    smiles_to_graph: callable, str -> DGLGraph
+        A function turning a SMILES string into a DGLGraph.
+        Default to :func:`dgllife.utils.smiles_to_bigraph`.
+    node_featurizer : callable, rdkit.Chem.rdchem.Mol -> dict
+        Featurization for nodes like atoms in a molecule, which can be used to update
+        ndata for a DGLGraph. Default to None.
+    edge_featurizer : callable, rdkit.Chem.rdchem.Mol -> dict
+        Featurization for edges like bonds in a molecule, which can be used to update
+        edata for a DGLGraph. Default to None.
+    load : bool
+        Whether to load the previously pre-processed dataset or pre-process from scratch.
+        ``load`` should be False when we want to try different graph construction and
+        featurization methods and need to preprocess from scratch. Default to False.
+    log_every : bool
+        Print a message every time ``log_every`` molecules are processed. Default to 1000.
+    cache_file_path : str
+        Path to the cached DGLGraphs, default to 'clintox_dglgraph.bin'.
+    n_jobs : int
+        The maximum number of concurrently running jobs for graph construction and featurization,
+        using joblib backend. Default to 1.
+
+    Examples
+    --------
+
+    >>> import torch
+    >>> from dgllife.data import ClinTox
+    >>> from dgllife.utils import smiles_to_bigraph, CanonicalAtomFeaturizer
+
+    >>> dataset = ClinTox(smiles_to_bigraph, CanonicalAtomFeaturizer())
+    >>> # Get size of the dataset
+    >>> len(dataset)
+    1484
+    >>> # Get the 0th datapoint, consisting of SMILES, DGLGraph, labels, and masks
+    >>> dataset[0]
+    ('*C(=O)[C@H](CCCCNC(=O)OCCOC)NC(=O)OCCOC',
+     Graph(num_nodes=24, num_edges=46,
+           ndata_schemes={'h': Scheme(shape=(74,), dtype=torch.float32)}
+           edata_schemes={}),
+     tensor([1., 0.]),
+     tensor([1., 1.]))
+
+    To address the imbalance between positive and negative samples, we can re-weight
+    positive samples for each task based on the training datapoints.
+
+    >>> train_ids = torch.arange(500)
+    >>> dataset.task_pos_weights(train_ids)
+    tensor([ 0.0684, 10.9048])
+    """
+    def __init__(self,
+                 smiles_to_graph=smiles_to_bigraph,
+                 node_featurizer=None,
+                 edge_featurizer=None,
+                 load=False,
+                 log_every=1000,
+                 cache_file_path='./clintox_dglgraph.bin',
+                 n_jobs=1):
+
+        self._url = 'dataset/clintox.zip'
+        data_path = get_download_dir() + '/clintox.zip'
+        dir_path = get_download_dir() + '/clintox'
+        download(_get_dgl_url(self._url), path=data_path, overwrite=False)
+        extract_archive(data_path, dir_path)
+        df = pd.read_csv(dir_path + '/clintox.csv')
+
+        super(ClinTox, self).__init__(df=df,
+                                      smiles_to_graph=smiles_to_graph,
+                                      node_featurizer=node_featurizer,
+                                      edge_featurizer=edge_featurizer,
+                                      smiles_column='smiles',
+                                      cache_file_path=cache_file_path,
+                                      load=load,
+                                      log_every=log_every,
+                                      init_mask=True,
+                                      n_jobs=n_jobs)
+
+    def __getitem__(self, item):
+        """Get datapoint with index
+
+        Parameters
+        ----------
+        item : int
+            Datapoint index
+
+        Returns
+        -------
+        str
+            SMILES for the ith datapoint
+        DGLGraph
+            DGLGraph for the ith datapoint
+        Tensor of dtype float32 and shape (T)
+            Labels of the ith datapoint for all tasks. T for the number of tasks.
+        Tensor of dtype float32 and shape (T)
+            Binary masks of the ith datapoint indicating the existence of labels for all tasks.
+        """
+        return self.smiles[item], self.graphs[item], self.labels[item], self.mask[item]

python/dgllife/data/csv_dataset.py

@@ -121,13 +121,17 @@ def _pre_process(self, smiles_to_graph, node_featurizer,
                 self.graphs = pmap(smiles_to_graph,
                                    self.smiles,
                                    node_featurizer=node_featurizer,
-                                   edge_featurizer=edge_featurizer)
+                                   edge_featurizer=edge_featurizer,
+                                   n_jobs=n_jobs)
+                self.graphs = [g for g in self.graphs if g is not None]
             else:
                 for i, s in enumerate(self.smiles):
                     if (i + 1) % log_every == 0:
                         print('Processing molecule {:d}/{:d}'.format(i+1, len(self)))
-                    self.graphs.append(smiles_to_graph(s, node_featurizer=node_featurizer,
-                                                       edge_featurizer=edge_featurizer))
+                    g = smiles_to_graph(s, node_featurizer=node_featurizer,
+                                        edge_featurizer=edge_featurizer)
+                    if g is not None:
+                        self.graphs.append(g)
             _label_values = self.df[self.task_names].values
             # np.nan_to_num will also turn inf into a very large number
             self.labels = F.zerocopy_from_numpy(np.nan_to_num(_label_values).astype(np.float32))